Application restore time from cloud gateway optimization using storlets

ABSTRACT

A method, computer system, and a computer program product for designing and executing at least one storlet is provided. The present invention may include receiving a plurality of restore operations based on a plurality of data. The present invention may also include identifying a plurality of blocks corresponding to the received plurality of restore operations from the plurality of data. The present invention may then include identifying a plurality of grain packs corresponding with the identified plurality of blocks. The present invention may further include generating a plurality of grain pack index identifications corresponding with the identified plurality of grain packs. The present invention may also include generating at least one storlet based on the generated plurality of grain pack index identifications. The present invention may then include returning a plurality of consolidated objects by executing the generated storlet.

BACKGROUND

The present invention relates generally to the field of computing, andmore particularly to cloud computing.

As a device that connects local applications to cloud-based storage,cloud storage gateways have rapidly evolved over the years to connect awide variety of users and applications (or operations) with a widevariety of functions to the cloud. Cloud storage gateways generallyinclude traditional object storage architecture, where the storage nodesstore an object as a full copy and replicate multiple copies of theobject, or erasure coding supported object storage architecture, wherethe storage nodes segment the object and store the object as erasurecode fragments. Optimization of the application restore time foroperations from the cloud to the storage area network (SAN) environmenthas become as an important aspect of the cloud computing.

SUMMARY

Embodiments of the present invention disclose a method, computer system,and a computer program product for designing and executing at least onestorlet. The present invention may include receiving a plurality ofrestore operations based on a plurality of data. The present inventionmay also include identifying a plurality of blocks corresponding to thereceived plurality of restore operations from the plurality of data. Thepresent invention may then include identifying a plurality of grainpacks corresponding with the identified plurality of blocks. The presentinvention may further include generating a plurality of grain pack indexidentifications corresponding with the identified plurality of grainpacks. The present invention may also include generating at least onestorlet based on the generated plurality of grain pack indexidentifications. The present invention may then include returning aplurality of consolidated objects by executing the generated storlet.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings. The various features of the drawings arenot to scale as the illustrations are for clarity in facilitating oneskilled in the art in understanding the invention in conjunction withthe detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2 is an operational flowchart illustrating a process for designingand executing a storlet according to at least one embodiment;

FIG. 3 is a block diagram illustrating the storlet compute engineembedded object storage architecture according to at least oneembodiment;

FIG. 4 is a snapshot of the uploaded virtual disk 1 block layoutaccording to at least one embodiment;

FIG. 5 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 6 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, in accordance with anembodiment of the present disclosure; and

FIG. 7 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 6, in accordance with an embodiment of thepresent disclosure.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of this invention to thoseskilled in the art. In the description, details of well-known featuresand techniques may be omitted to avoid unnecessarily obscuring thepresented embodiments.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The following described exemplary embodiments provide a system, methodand program product for designing and executing a storlet. As such, thepresent embodiment has the capacity to improve the technical field ofcloud computing by identifying the metadata on the SAN environment,sending the metadata to a storlet generated by the storlet design andexecution program, utilizing the generated storlet to determine theobject, and returning the object to operate on the metadata. Morespecifically, the storlet design and execution program may receive therestore operation of a particular file or fragment, and then may processand prepare block addresses with a file or fragment utilizing a knownfilesystem. The storlet design and execution program may then identifycorresponding grain packs and generate grain pack index identificationsbased on a metadata table. Then, the grain pack index identificationsand the metadata table may be used to generate a storlet, whichconsolidates the grain pack index identifications into a single object,which is returned by the restore operation.

As described previously, as a device that connects local applications tocloud-based storage, cloud storage gateways have rapidly evolved overthe years to connect a wide variety of users and applications (oroperations) with a wide variety of functions to the cloud. Cloud storagegateways generally include traditional object storage architecture,where the storage nodes store an object as a full copy and replicatemultiple copies of the object, or erasure coding supported objectstorage architecture, where the storage nodes segment the object andstore the object as erasure code fragments. Optimization of theapplication restore time for operations from the cloud to the storagearea network (SAN) environment has become as an important aspect of thecloud computing.

Therefore, it may be advantageous to, among other things, create aframework with an algorithm that may be integrated with traditional SANcloud gateway to compute embedded object storage infrastructure and mayoffer a secure, automated way of storlet design and execution that helpsin optimizing, reducing cost, response and time of restore operationsfrom cloud to SAN environment.

According to at least one embodiment, the storlet design and executionprogram may provide flexibility to the traditional SAN cloud gateway andcompute embedded object storage infrastructure by utilizing atransparent cloud tiering/cloud gateway, and object storagearchitecture.

According to at least one embodiment, the transparent cloud tiering mayallow data center administrators to free up on-premises storagecapacity, by moving out cooler data (i.e., less active data that israrely used or accessed), where the cooler data can reside on SAN unitsor network attached storage (NAS) units, to the cloud storage, therebyreducing capital and operational expenditures.

According to at least one embodiment, the storlet design and executionprogram may identify the metadata (i.e., summarizes basic informationabout data that makes finding and working with particular instances ofdata easier) on the SAN environment and send the metadata to a newlycreated storlet that determines the object. Then, the object may operateby utilizing the metadata.

According to at least one embodiment, the storlet design and executionprogram may also identify the blocks corresponding to the file orfragment, which is requested for restoration, may identify the grains(i.e., multiple blocks or logical block addresses that are groupedtogether) or grain packs (i.e., multiple grains grouped together) underwhich the blocks corresponding to the object are packaged, and performmulti-byte read across different objects by a generated storlet andprepare a consolidated object or perform an offline rearrangement ofblocks within the objects.

According to at least one embodiment, the storlet design and executionprogram may identify the blocks corresponding to a file or fragment thatare requested for restoration. The storlet design and execution program,residing in the SAN storage, may prepare a block map (i.e., block mapmay spread across multiple disks, pools or volumes) corresponding to asingle virtual machine disk (VMDK) file or erasure code (EC) fragment,which may have to be restored from the cloud. The block map maycorrespond to a list of logical block addresses, which may be restoredfrom the cloud. The list of logical block addresses may be stored asgrains and further stored as grain packs or objects (i.e., group ofgrain packs) in the cloud. Utilizing the obtained block map and thegrain pack metadata, the traditional restore operation may triggermultiple object GET operations (i.e., permits the user to request aresource and obtain the content information along with the resource). Inanother embodiment, the traditional restore operations may trigger asingle GET request in which the request may be appended with queryingmetadata.

According to at least one embodiment, the storlet design and executionprogram may identify whether the grains or grain packs associated withthe blocks corresponding to the object are packaged. Upon receivingmultiple parallel object GET requests or a single GET request withappended block map metadata, the storlet design and execution programresiding in the object storage controller (i.e., a processor orprocessors embedded to perform a wide range of functions related to thestorage system) as middleware may perform validation checks tounderstand if these requests were made by the traditional restoreoperation to reconstruct a VMDK file or EC fragment. If the requests areidentified, then the proposed middleware (i.e., software that acts as abridge between an operating system or database and applications,especially on a network) may start preparing the storlet with inputbased on the received block map and grain pack metadata.

According to at least one embodiment, the storlet design and executionprogram may perform multi-byte read across different objects and mayprepare a consolidated object or perform an offline rearrangement ofblocks within the objects. The auto-generated storlet may perform aselective multi-byte read of blocks corresponding to the block map inputfrom each object, where the grain pack selection for read are dependenton grain pack metadata, and may consolidate the multiple blocks to asingle object that may be sent in response to a GET request made by therestore operation.

Referring to FIG. 1, an exemplary networked computer environment 100 inaccordance with one embodiment is depicted. The networked computerenvironment 100 may include a computer 102 with a processor 104 and adata storage device 106 that is enabled to run a software program 108and a storlet design and execution program 110 a. The networked computerenvironment 100 may also include a server 112 that is enabled to run astorlet design and execution program 110 b that may interact with adatabase 114 and a communication network 116. The networked computerenvironment 100 may include a plurality of computers 102 and servers112, only one of which is shown. The communication network 116 mayinclude various types of communication networks, such as a wide areanetwork (WAN), local area network (LAN), a telecommunication network, awireless network, a public switched network and/or a satellite network.It should be appreciated that FIG. 1 provides only an illustration ofone implementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

The client computer 102 may communicate with the server computer 112 viathe communications network 116. The communications network 116 mayinclude connections, such as wire, wireless communication links, orfiber optic cables. As will be discussed with reference to FIG. 5,server computer 112 may include internal components 902 a and externalcomponents 904 a, respectively, and client computer 102 may includeinternal components 902 b and external components 904 b, respectively.Server computer 112 may also operate in a cloud computing service model,such as Software as a Service (SaaS), Platform as a Service (PaaS), orInfrastructure as a Service (IaaS). Server 112 may also be located in acloud computing deployment model, such as a private cloud, communitycloud, public cloud, or hybrid cloud. Client computer 102 may be, forexample, a mobile device, a telephone, a personal digital assistant, anetbook, a laptop computer, a tablet computer, a desktop computer, orany type of computing devices capable of running a program, accessing anetwork, and accessing a database 114. According to variousimplementations of the present embodiment, the storlet design andexecution program 110 a, 110 b may interact with a database 114 that maybe embedded in various storage devices, such as, but not limited to acomputer/mobile device 102, a networked server 112, or a cloud storageservice.

According to the present embodiment, a user using a client computer 102or a server computer 112 may use the storlet design and executionprogram 110 a, 110 b (respectively) to design and execute a storlet toimprove application restore time from cloud gateway optimization. Thestorlet design and execution method is explained in more detail belowwith respect to FIGS. 2 and 3.

Referring now to FIG. 2, an operational flowchart illustrating theexemplary storlet design and execution process 200 used by the storletdesign and execution program 110 a and 110 b according to at least oneembodiment is depicted.

At 202, the restore operation of a particular file or fragment isreceived. A user may request a read operation of a VMDK file or ECfragment, where the file structure is maintained by a known file systemusing SAN volume or a storage derived from a SAN unit. Using a softwareprogram 108 on the user's device (e.g., user's computer 102), the readoperation of the VMDK file or EC fragment may be transmitted as inputinto the storlet design and execution program 110 a, 110 b via acommunication network 116. Alternatively, the user may manually inputthe read operation of the VMDK file or EC fragment into the storletdesign and execution program 110 a and 110 b. The read operation of theVMDK file or EC fragment may then be translated to recall groups ofblocks associated with a block map (i.e., grouping of block addresses).

For example, a user selects a public cloud for tiering cold data, suchas snapshots and VMDK files. As such, the user manually uploads a file“Asnapshot1.vmdk” into the storlet design and execution program 110 a,110 b. The VMDK file is transmitted to the storlet design and executionprogram 110 a, 110 b via a communication network 116.

A snapshot of the uploaded virtual disk 1 block layout will be describedin greater detail below with respect to FIG. 4.

The following Table 1 shows another representation of the virtual disk1, which includes the file names, size listed on the virtual machinefile system (VMFS), and the block address (i.e., address location onVirtual Disk 1), and further shows the relation established between theobject (stored in the cloud) with the logical block addresses (LBA):

TABLE 1 Address Location on Virtual Disk1 File Name Size listed on VMFS(spread across 3 disks) A vmdk 100 GB   0-50K B snapshot 1 vmdk 30 GB 50K-120K B vmdk 10 GB 120K-400K A snapshot 2 vmdk 500 GB 400K-600K Bsnapshot 2 vmdk 100 GB 600K-650K A snapshot 1 vmdk 200 GB 650K-800K

Additionally, the user performs a restore operation of a virtual machine“A” files, including “Asnapshot1.vmdk,” due to the random nature ofarranging blocks in to multiple grains and in turn into multiple grainpacks, the data blocks corresponding to the “Asnapshot1.vmdk” may spreadacross multiple objects.

In another embodiment, the read operation of the VMDK file or ECfragment may be received as system calls by the storlet design andexecution program 110 a and 110 b in the following format: “open(‘file1’, w+).”

Next, at 204, the block addresses with the file or fragment areprocessed and prepared. The storlet design and execution program 110 a,110 b may process and prepare the block addresses that correspond to thefile or fragment by utilizing a known filesystem. The storlet design andexecution program 110 a, 110 b may initiate a read/open call to thefilesystem that fetches the block addresses. The process for fetchingthe block addresses may be based on a dynamic algorithm that determinesthe block addresses from the inode details and the super block. Thefilesystem may track, or maintain a known algorithm, associated with thefile or fragment corresponding with block addresses. The output of thefilesystem may be a description of the received data (e.g., whether thereceived data is a file associated with replication or fragmentassociated with erasure code), and an identification of the blockaddresses corresponding to the file or fragment.

Continuing the previous example, the storage controller is capable ofperforming cold data tiering to the cloud as well as virtualizingunderlying storage. As such, the virtual disk created from the describedstorage controller is mapped to a hypervisor that created the filesystemusing the virtual disk to create multiple VMDK files. Additionally, theoutput of the processing and preparing of the block addresses is thatthe storlet design and execution program 110 a, 110 b may determine thatAsnapshot1.vmdk is a file association with replication and that 650k-800 k are the block addresses associated with Asnapshot1.vmdk.

In another embodiment, if the storage controller is capable ofperforming cold data tiering to the cloud and acts as a storage unit forerasure code supported object storage, then multiple virtual volumes maybe created from the storage controller and mapped in storage nodes ofthe erasure code supported object storage cluster.

Then, at 206, the corresponding grain packs are identified. Based on theblock addresses, a metadata table (i.e., a data structure that thestorage controller may utilize to identify which of the blocks migratedto the cloud) may be utilized to identify the corresponding grain packswith the blocks (i.e., grains). When the user requests blocks, thestorage controller may check the metadata table to determine whether theblocks are residing on-premises or in the cloud. If the blocks arestored in the cloud, then the objects with the respective blocks may berecalled with the corresponding grain packs.

The metadata table may be generated by a back-up algorithm that may betriggered for one time use by utilizing a command line interface (CLI)or runs continuously as a daemon. In the initial point, the metadatatable may be an empty table in the database 114 with schema initializedgenerated. The back-up algorithm may crawl through the blocks topopulate the table (i.e., crawling is parallel and each parallel threadmay select different block ranges).

The metadata table may include the object name, the grain pack indexidentification (i.e., grain pack ID), the grain pack indexidentification per grain pack (i.e., Grain IDs per Grain Pack), thelogical block addresses packed per grain (i.e., LBA's packed per grain),and the grain compressed (i.e., a flag to indicate if compression may beenabled for the set of blocks). Referring to the “grain compressed”column of the metadata table, for example, if the “grain compressed”column indicates “True,” then the blocks grouped under each grain may becompressed in which the space is reduced from “00000” to “5*0.” If thegrain compressed column indicates “False,” then the blocks may beuncompressed.

Continuing the previous example, the following Table 2 is the metadatatable generated by a command line interface and frequently updated:

TABLE 2 Grain Object Name Grain Pack ID Grain IDs per Grain pack LBA'spacked per grain (8 Bit) Compressed 0000 0000 0, 1, 4, 6, 7, 9 0(00000-00007), 1 (00008-00015), 4 (00032- False 00047), 6 (00056-00063),7 (00064-00071), 9 (00088-00095) 0001 0001 100, 200, 800, 150, 750, 900100 (00800-00807), 200 (01600-01607), 800 True (06400-06407), 150(01200-01207), 750 (06000-06007), 900 (07200-07207) 0002 0002 101, 90,89, 56, 57, 29 101 (00808-00815), 90 (00720-00727), 89 True(00712-00719), 56 (00448-00456), 57 (00456- 00463), 29 (00232-00239)0003 0003 2000, 10, 2, 550, 701, 802 2000 (16000-160007), 10(00000-00007), 2 True (00016-00023), 650 (05200-05207), 701(05608-05615), 802 (06416-06423) 0004 0004 3, 95, 14, 34, 3454, 19 3(00024-00031), 95 (00760-00767), 14 True (00112-00119), 34(00272-00279), 3454 (27632-27637), 19 (00152-00159) 0005 0005 201, 208,304, 601, 307, 309 201 (01608-01615), 208 (01664-01671), 304 False(02432-02439), 601 (04808-04815), 307 (02456-02463), 309 (02472-02479)

The metadata table is utilized to identify the corresponding grain packswith the blocks. For Asnapshot1.vmdk, the corresponding grain packsinclude 650, 701, 750 and 800 based on the metadata table shown in theabove Table 2.

In another embodiment, the metadata table may be generated by a commandline interface (e.g., admin CLI) or by a policy that may determine thecriteria of migration to the cloud and update the metadata table, whichmay be stored in the storage controller. Additionally, a duplicate ofthe metadata table may be stored in the cloud as well as on-premises.

Then, at 208, the grain pack index identifications are generated. Basedon the identification of the grain packs associated with the blocks, thestorlet design and execution program 110 a, 110 b may generate the grainpack index identification. The grain pack index identification may begenerated based on the administrator initial configuration. Theadministrator, for example, may configure the initial value as 500 andthe index may be incremented accordingly. Then, the storlet design andexecution program 110 a, 110 b may identify the object namecorresponding with the grain pack index identification. Each objectstored in the cloud may have metadata, where the grain pack indexidentification is stored. A generic database operation, for example,“select” may be utilized to search across the metadata table forcorresponding grain pack index identification.

Continuing the previous example, the storlet design and executionprogram 110 a, 110 b generated 0001 and 0003 as the grain pack indexidentification for Asnapshot1.vmdk based on the metadata table shownabove as Table 3. The grain pack index identifications are associatedwith the grain packs. Grain packs 750 and 800 are associated with grainpack index identification 0001, and grain packs 650 and 701 areassociated with grain pack index identification 0003.

In another embodiment, the user may request blocks that spread acrossmultiple objects. The storlet design and execution program 110 a, 110 bmay collect the grains from each related object and prepare anotherfinal object.

Then, at 210, the storlet is generated and executed. The storlet designand execution program 110 a, 110 b may utilize the generated grain packindex identifications to search the metadata table to dynamically createat least one storlet (i.e., an object service extension that allows auser defined code to run inside the object store in a secure andisolated manner through the use of containers), which may perform theselective multi-byte read within the objects corresponding to requestedgrain pack index identifications and consolidate the requested grainpack index identifications to a single object. Once the block range isidentified in the SAN environment, the grain packs may be sent to thestorlet, which is located on the cloud. The storlet may read each objectstored on the cloud store to determine whether the grain pack indexidentification matches the object checked by the user. If the grain packindex identification and the object matches, then the ranges may be readand consolidated into a corresponding object for data use.

The storlet may bring compute to storage as opposed to a traditionalmethod of pulling data from the object storage to client nodes forcomputation. The storlet may be a piece of middleware, which may invokeon PUT operation. The content or metadata of object PUT may analyzed anddetermine whether the metadata is an algorithm or a data object.

Continuing the previous example, the storlet design and executionprogram 110 a, 110 b creates a storlet based on the grain pack indexidentifications of 0001 and 0003, and the supporting data forAsnapshot1.vmdk on the metadata table. The generated storlet performs aselective multi-byte read with objects 0001 and 0003, which correspondwith grain pack index identifications 0001 and 0003, respectively, andconsolidates the two requested grain pack index identifications (e.g.,0001 and 0003) into a single object: 00013.

In the present embodiment, the generated object may be temporary.Alternatively, if the administrator determines that the same blocks arefrequently recalled by the application or the user, the administratormay change the configuration to preclude the deletion of the generatedobject.

In another embodiment, if the cloud user deletes the object in the cloudstore, then the grain pack identification and the object may not match.The storlet may then respond to the storage controller as “NULL,” whichmay return an error message, for example “unrecoverable or missing,” tothe user.

Then, at 212, the prepared object is returned. The storlet design andexecution program 110 a, 110 b may return the corresponding objects inresponse to the GET request made by the restore operation. The generatedstorlet may be deployed into a set of proxy nodes (i.e., utilized for adistributed load handling/request handling nodes into the namespace) andstorage nodes (i.e., responsible for writing in to the disks or storagesubsystems to store the requests) and returns the corresponding preparedobject that may be present or absent on-premises or in the cloud. Theblock diagram illustrating the storlet compute engine embedded objectstorage architecture will be described in greater detail below withrespect to FIG. 3.

Additionally, the prepared object may be unpacked and the grains may beread using “load” instruction or “read” call from the unpacked preparedobject, the grains may be identified from the metadata table and may beserved to file a read request from the user.

Continuing the previous example, the object prepared by the storlet(e.g., 00013) is returned by the cloud and the metadata table is updatedaccordingly to include the new object and the corresponding grain packindex identifications and logical block addresses.

In another embodiment, if the generated storlet is deployed into a setof proxy and storage nodes and at least one corresponding object is notreturned, then the storlet design and execution program 110 a, 110 b maypresent an error message and may suspend operations until resolved.

Referring now to FIG. 3, a block diagram illustrating the exemplarystorlet compute engine embedded object storage architecture 300 used bythe storlet design and execution program 110 a and 110 b according to atleast one embodiment is depicted. As shown, the storlet design andexecution program 110 a, 110 b utilizes load balancing to distributedata across multiple computing resources (e.g., computers, computerclusters, network links, central processing units or disk drives) tooptimize resource use, maximize throughput, minimize response time andavoid the overloading of a single resource.

Cloud storage gateway may provide the essential infrastructure toback-up as an object and restore as an object. The SAN back-up algorithmresiding in the controller may be responsible for packing the blocks asobjects and passing the blocks to the cloud gateway. Similarly, thecloud storage may be responsible to hand-over the object to restore theengine. The storlet or micro services (e.g., a container) may be locatedat the cloud end in which the storlet may operate on multiple objects(i.e., selecting block ranges) and may prepare the final/end object thatincludes the blocks for the VMDK or EC fragment file. The created endobject may be passed to restore (or object PUT) request triggered by thecloud gateway.

The embedded storlet compute engine built-in object storage architecture300 includes a load balancer 304 (i.e., receives the incoming storlet),and a software application present within the proxy nodes 308 andstorage nodes 310. The users 302 a and 302 b may frame the storlet andmay deploy or pass the storlet to the storlet design and executionprogram 110 a, 110 b as a normal object PUT operation (i.e., operationutilized to create or update). No additional client or compute node maybe included for the storlet design and execution program 110 a, 110 b toanalyze the data, since each object proxy node 308 and storage node 310includes a VM 306 with a known computing algorithm to retrieve theobject. As such, the proxy nodes 308 and storage nodes 310 may act asthe compute node and return results to the users 302 a and 302 b.

Additionally, on each proxy node 308 and storage node 310, there arevirtualization units (i.e., VM) 306 that act as thecomputation/processing engines for each of the nodes. The VM 306 orembedded computation engines with nodes are used for analysis andcomputation. As such, no external compute node for processing may beutilized with the storlet design and execution program 110 a, 110 b.

Additionally, the storlet compute engine embedded object storagearchitecture 300 may utilize virtualization technology (e.g.,kernel-based virtual machine (KVM), containers, or open source lightweight virtualization and sandboxing technology (ZeroVM)) deployed onthe nodes to perform the computation tasks.

In another embodiment, the user may off-load the compute algorithm tostorage rather than reading data from storage to a compute node forprocessing.

In another embodiment, the two users 302 a and 302 b enter inputrequests (in the form of algorithms) into the storlet design andexecution program 110 a, 110 b. The received files or fragments aretransmitted to a load balancer 304, which receives the incoming requestsand shares the requests to proxy nodes 308 and storage nodes 310. Proxynodes 308 are utilized for a distributed load handling/request handlingnodes into the namespace and other node groups (i.e., storage nodes310). Storage nodes 310 are responsible for writing in to the disks orstorage subsystems to store the requests.

Referring now to FIG. 4, a block diagram 400 illustrating the exemplarysnapshot of the uploaded virtual disk 1 block layout, which spreadsacross three disks.

It may be appreciated that FIGS. 2-4 provide only an illustration of oneembodiment and do not imply any limitations with regard to how differentembodiments may be implemented. Many modifications to the depictedembodiment(s) may be made based on design and implementationrequirements.

FIG. 5 is a block diagram 900 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment of the present invention. It should be appreciated that FIG.5 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

Data processing system 902, 904 is representative of any electronicdevice capable of executing machine-readable program instructions. Dataprocessing system 902, 904 may be representative of a smart phone, acomputer system, PDA, or other electronic devices. Examples of computingsystems, environments, and/or configurations that may represented bydata processing system 902, 904 include, but are not limited to,personal computer systems, server computer systems, thin clients, thickclients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, network PCs, minicomputer systems, anddistributed cloud computing environments that include any of the abovesystems or devices.

User client computer 102 and network server 112 may include respectivesets of internal components 902 a, b and external components 904 a, billustrated in FIG. 5. Each of the sets of internal components 902 a, bincludes one or more processors 906, one or more computer-readable RAMs908, and one or more computer-readable ROMs 910 on one or more buses912, and one or more operating systems 914 and one or morecomputer-readable tangible storage devices 916. The one or moreoperating systems 914, the software program 108 and the storlet designand execution program 110 a in client computer 102, and the storletdesign and execution program 110 b in network server 112, may be storedon one or more computer-readable tangible storage devices 916 forexecution by one or more processors 906 via one or more RAMs 908 (whichtypically include cache memory). In the embodiment illustrated in FIG.5, each of the computer-readable tangible storage devices 916 is amagnetic disk storage device of an internal hard drive. Alternatively,each of the computer-readable tangible storage devices 916 is asemiconductor storage device such as ROM 910, EPROM, flash memory or anyother computer-readable tangible storage device that can store acomputer program and digital information.

Each set of internal components 902 a, b also includes a R/W drive orinterface 918 to read from and write to one or more portablecomputer-readable tangible storage devices 920 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 and the storlet design and execution program 110 a and 110 bcan be stored on one or more of the respective portablecomputer-readable tangible storage devices 920, read via the respectiveR/W drive or interface 918, and loaded into the respective hard drive916.

Each set of internal components 902 a, b may also include networkadapters (or switch port cards) or interfaces 922 such as a TCP/IPadapter cards, wireless Wi-Fi interface cards, or 3G or 4G wirelessinterface cards or other wired or wireless communication links. Thesoftware program 108 and the storlet design and execution program 110 ain client computer 102 and the storlet design and execution program 110b in network server computer 112 can be downloaded from an externalcomputer (e.g., server) via a network (for example, the Internet, alocal area network or other, wide area network) and respective networkadapters or interfaces 922. From the network adapters (or switch portadaptors) or interfaces 922, the software program 108 and the storletdesign and execution program 110 a in client computer 102 and thestorlet design and execution program 110 b in network server computer112 are loaded into the respective hard drive 916. The network maycomprise copper wires, optical fibers, wireless transmission, routers,firewalls, switches, gateway computers and/or edge servers.

Each of the sets of external components 904 a, b can include a computerdisplay monitor 924, a keyboard 926, and a computer mouse 928. Externalcomponents 904 a, b can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 902 a, b also includes device drivers930 to interface to computer display monitor 924, keyboard 926, andcomputer mouse 928. The device drivers 930, R/W drive or interface 918,and network adapter or interface 922 comprise hardware and software(stored in storage device 916 and/or ROM 910).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 6, illustrative cloud computing environment 1000is depicted. As shown, cloud computing environment 1000 comprises one ormore cloud computing nodes 100 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 1000A, desktop computer 1000B, laptopcomputer 1000C, and/or automobile computer system 1000N may communicate.Nodes 100 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 1000to offer infrastructure, platforms and/or software as services for whicha cloud consumer does not need to maintain resources on a localcomputing device. It is understood that the types of computing devices1000A-N shown in FIG. 6 are intended to be illustrative only and thatcomputing nodes 100 and cloud computing environment 1000 can communicatewith any type of computerized device over any type of network and/ornetwork addressable connection (e.g., using a web browser).

Referring now to FIG. 7, a set of functional abstraction layers 1100provided by cloud computing environment 1000 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 7 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 1102 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 1104;RISC (Reduced Instruction Set Computer) architecture based servers 1106;servers 1108; blade servers 1110; storage devices 1112; and networks andnetworking components 1114. In some embodiments, software componentsinclude network application server software 1116 and database software1118.

Virtualization layer 1120 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers1122; virtual storage 1124; virtual networks 1126, including virtualprivate networks; virtual applications and operating systems 1128; andvirtual clients 1130.

In one example, management layer 1132 may provide the functionsdescribed below. Resource provisioning 1134 provides dynamic procurementof computing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 1136provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 1138 provides access to the cloud computing environment forconsumers and system administrators. Service level management 1140provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 1142 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 1144 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 1146; software development and lifecycle management 1148;virtual classroom education delivery 1150; data analytics processing1152; transaction processing 1154; and storlet design and execution1156. A storlet design and execution program 110 a, 110 b provides a wayto design and execute a storlet to improve application restore time fromcloud gateway optimization.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method comprising:identifying a plurality of grain packs corresponding with a plurality ofblocks; generating a plurality of grain pack index identificationscorresponding with the identified plurality of grain packs; generatingat least one storlet based on the generated plurality of grain packindex identifications; and returning a plurality of consolidated objectsby executing the generated storlet, wherein the generated at least onestorlet is deployed as a normal object PUT operation into a plurality ofstorage nodes and a plurality of proxy nodes.
 2. The method of claim 1,further comprising: receiving a plurality of restore operations based ona plurality of data; and identifying the plurality of blockscorresponding to the received plurality of restore operations from theplurality of data.
 3. The method of claim 2, wherein receiving theplurality of restore operations based on the plurality of data, furthercomprises: determining the received plurality of data is a plurality offiles.
 4. The method of claim 2, wherein receiving the plurality ofrestore operations based on the plurality of data, further comprises:determining the received plurality of data is a plurality of erasurecode fragments.
 5. The method of claim 1, wherein identifying theplurality of grain packs corresponding with the identified plurality ofblocks, further comprises: utilizing a metadata table to determine theidentified plurality of grain packs associated with the identifiedplurality of blocks.
 6. The method of claim 1, further comprising:performing a selective multi-byte read operation with a plurality ofobjects associated with the generated plurality of grain pack indexidentifications by the generated storlet; and consolidating thegenerated plurality of grain pack index identifications to a singleobject.
 7. The method of claim 1, wherein returning the plurality ofconsolidated objects by executing the generated storlet, furthercomprises: analyzing the deployed storlet by a plurality ofvirtualization units associated with the plurality of storage nodes andthe plurality of proxy nodes.
 8. The method of claim 7, furthercomprising: determining that the returned plurality of consolidatedobjects is absent; and returning an error message to the user.
 9. Themethod of claim 7, further comprising: determining that the returnedplurality of consolidated objects is present; and returning theplurality of consolidated objects by executing the generated storlet.10. A computer system for designing and executing at least one storlet,comprising: one or more processors, one or more computer-readablememories, one or more computer-readable tangible storage medium, andprogram instructions stored on at least one of the one or more tangiblestorage medium for execution by at least one of the one or moreprocessors via at least one of the one or more memories, wherein thecomputer system is capable of performing a method comprising:identifying a plurality of grain packs corresponding with a plurality ofblocks; generating a plurality of grain pack index identificationscorresponding with the identified plurality of grain packs; generatingat least one storlet based on the generated plurality of grain packindex identifications; and returning a plurality of consolidated objectsby executing the generated storlet, wherein the generated at least onestorlet is deployed as a normal object PUT operation into a plurality ofstorage nodes and a plurality of proxy nodes.
 11. The computer system ofclaim 10, further comprising: receiving a plurality of restoreoperations based on a plurality of data; and identifying the pluralityof blocks corresponding to the received plurality of restore operationsfrom the plurality of data.
 12. The computer system of claim 11, whereinreceiving the plurality of restore operations based on the plurality ofdata, further comprises: determining the received plurality of data is aplurality of files.
 13. The computer system of claim 11, whereinreceiving the plurality of restore operations based on the plurality ofdata, further comprises: determining the received plurality of data is aplurality of erasure code fragments.
 14. The computer system of claim10, wherein identifying the plurality of grain packs corresponding withthe identified plurality of blocks, further comprises: utilizing ametadata table to determine the identified plurality of grain packsassociated with the identified plurality of blocks.
 15. The computersystem of claim 10, further comprising: performing a selectivemulti-byte read operation with a plurality of objects associated withthe generated plurality of grain pack index identifications by thegenerated storlet; and consolidating the generated plurality of grainpack index identifications to a single object.
 16. The computer systemof claim 10, wherein returning the plurality of consolidated objects byexecuting the generated storlet, further comprises: analyzing thedeployed storlet by a plurality of virtualization units associated withthe plurality of storage nodes and the plurality of proxy nodes.
 17. Thecomputer system of claim 16, further comprising: determining that thereturned plurality of consolidated objects is absent; and returning anerror message to the user.
 18. The computer system of claim 16, furthercomprising: determining that the returned plurality of consolidatedobjects is present; and returning the plurality of consolidated objectsby executing the generated storlet.
 19. A computer program product fordesigning and executing at least one storlet, comprising: one or morecomputer-readable storage tangible media and program instructions storedon at least one of the one or more non-transitory computer readablemedium, the program instructions executable by a processor to cause theprocessor to perform a method comprising: identifying a plurality ofgrain packs corresponding with a plurality of blocks; generating aplurality of grain pack index identifications corresponding with theidentified plurality of grain packs; generating at least one storletbased on the generated plurality of grain pack index identifications;and returning a plurality of consolidated objects by executing thegenerated storlet, wherein the generated at least one storlet isdeployed as a normal object PUT operation into a plurality of storagenodes and a plurality of proxy nodes.
 20. The computer program productof claim 19, further comprising: receiving a plurality of restoreoperations based on a plurality of data; and identifying the pluralityof blocks corresponding to the received plurality of restore operationsfrom the plurality of data.